deal-intel-mcp
by JrJuni
README.md
# recruit-ai-mcp
**English** | [Korean](README.ko.md)
Recruit AI MCP is a bootstrap fork of Deal Intelligence MCP for recruiter and
search-firm intelligence.
This repository is currently a staged recruiting fork:
- Public package metadata, CLI alias, config paths, env prefix, and MongoDB
defaults now use `recruit-ai-mcp` / `recruit-ai` / `RECRUIT_AI_*`.
- Python package imports intentionally remain `deal_intel`.
- The MCP tool surface now includes first recruiting workflows for candidates,
client companies, positions, feedback, recommendations, metrics, reports,
and demo data.
- Inherited deal-intelligence tools remain available during the staged cutover.
- Default MongoDB databases are `recruit_ai` and `recruit_ai_demo`; M0 uses the
existing Python cosine search path, not Atlas Vector Search.
Recruit AI MCP is a self-owned recruiting memory and recommendation layer for
solo recruiters, search firms, and small hiring teams.
It stores candidate profiles, client companies, open positions, submissions,
client feedback, recruiter interactions, and recommendation runs in a database
you control. It then turns that structured evidence into transparent fit
signals: who matches a role, which roles fit a candidate, what is missing, what
risks should be reviewed, and how client feedback changes the ranking.
For mature recruiting platforms, it is not an ATS replacement. For teams whose
current system is scattered resumes, spreadsheets, intake notes, email threads,
and memory, it can act as a lightweight recruiting intelligence layer: your
data, your MongoDB or local storage, your fit criteria, queried from Claude
Desktop, Codex, or another MCP-capable host.
The inherited Deal Intelligence MCP workflows remain available as a
compatibility layer during the staged cutover. Those tools still support deal
records, qualification evidence, customer themes, product context, and weekly
sales reports for users who need them while the recruiting surface matures.
The default operating path is MongoDB Atlas-backed `full` mode, including the
free/M0 tier. A bundled no-MongoDB `sample` mode exists for AI agents, quick
evaluation, and demos, but real team use should start from `full`.
Start here:
- Setup with an AI assistant: [`AI_START_HERE.md`](AI_START_HERE.md).
- Longer full-mode walkthrough: [`AI_FULL_INSTALL_GUIDE.md`](AI_FULL_INSTALL_GUIDE.md).
- Public demo prompts: [`docs/public-demo-script.md`](docs/public-demo-script.md).
- First external tester handoff: [`AI_USER_TEST_GUIDE.md`](AI_USER_TEST_GUIDE.md).
- Fork/customize the project: [`docs/extending.md`](docs/extending.md),
[`docs/customization-recipes.md`](docs/customization-recipes.md), then
[`docs/architecture.md`](docs/architecture.md).
- Korean full-mode walkthrough: [`AI_FULL_INSTALL_GUIDE.ko.md`](AI_FULL_INSTALL_GUIDE.ko.md).
---
## What it does
- Stores recruiting records for candidates, client companies, positions,
submissions, feedback, interactions, and recommendation runs.
- Recommends candidates for positions and positions for candidates using
deterministic fit scoring plus M0-safe lexical retrieval.
- Reports recruiting pipeline counts, funnel status, feedback signals, and
local Markdown/CSV recruiting reports.
- Keeps inherited deal records and customer evidence available in your MongoDB
Atlas database, or in local sample/personal storage for zero-config trials
during the staged cutover.
- Preserves inherited deal-intelligence compatibility for structured deal
fields, health signals, follow-up gaps, customer themes, and weekly review
artifacts.
- Keeps inherited product/solution context available for compatibility deal
workflows, such as ICP notes, positioning, pricing notes, security claims,
integrations, and competitor notes.
- Lets an AI host answer normal recruiting questions such as "which candidates
best match this role?", "which roles fit this candidate?", or "generate a
recruiting pipeline report".
- Keeps read-only BI, review, reporting, and export paths deterministic and
LLM-free, so the host app can narrate results without extra server-side model
calls.
- Exposes diagnostics (`config_doctor`, storage checks, usage/cost summaries)
instead of hiding setup and data-quality problems.
## What it is not
- It is not an autonomous recruiter. It structures evidence; you still make
the shortlist, outreach, and hiring judgment.
- It is not yet a mature CRM suite with permissions, contact ownership,
calendar/email sync, workflow automation, or enterprise integrations.
- It is not a hosted SaaS that owns your recruiting/team data. The normal full
path uses your MongoDB Atlas project and your selected LLM provider.
- It does not claim revenue lift numbers. If the data is incomplete, it says so.
## Architecture At A Glance
This is not just a prompt wrapper. It is a small recruiting intelligence
backend exposed through MCP, with inherited deal-intelligence compatibility
tools still present during the staged cutover:
```text
[AI host: Claude / Codex / ChatGPT]
|
v
[MCP tool surface]
|
v
[Domain service layer]
|-- candidates, client companies, and positions
|-- recruiting interactions, submissions, and feedback
|-- fit scoring, lexical retrieval, and recommendations
|-- recruiting metrics and reports
|-- compatibility deal intake and sales reports
`-- export, config, and diagnostics
|
v
[Storage and retrieval]
|-- MongoDB Atlas full mode
|-- local sample/personal mode
`-- Atlas Vector Search pro mode
```
A normal recruiting recommendation path looks like this:
```text
1. Candidate, client-company, and position records enter through recruiting tools.
2. Interactions, submissions, and client feedback add evidence over time.
3. The deterministic fit builder compares candidate and position signals.
4. Client feedback rubric deltas adjust visible dimensions with an audit ledger.
5. M0-safe lexical retrieval narrows pools before final fit scoring.
6. The MCP host narrates the recommendation, risks, and next questions.
```
An inherited deal-intelligence write path looks like this:
```text
1. A meeting note, email reply, interview, or call summary enters through add_interaction.
2. The server-side LLM extracts structured deal signals from customer evidence.
3. Raw evidence, source metadata, and derived qualification fields are stored.
4. Deal summaries and product-context chunks can be embedded for retrieval.
5. Read-only tools compute metrics, gaps, reports, themes, and exports without extra LLM calls.
6. The MCP host narrates the final answer to the user.
```
Important boundaries:
- MCP is the interface layer; Claude, Codex, or ChatGPT can be the UI.
- Customer evidence is separated from derived intelligence such as health,
gaps, themes, and qualification snapshots.
- LLM-heavy paths are mostly write/enrichment paths. Read/report/export paths
are deterministic where possible.
- Product context is seller-side RAG context. It helps interpretation but is
not counted as customer-stated evidence.
- `sample -> full -> pro` is the scaling path: zero-config trial, MongoDB-backed
real data, then Atlas Vector Search when paid infrastructure is intentional.
- Tool surfaces are profile-filtered so normal users, sample users, and
maintainers do not need the same visible tool set.
For the deeper module map, read [`docs/architecture.md`](docs/architecture.md).
For fork/customization entry points, read [`docs/extending.md`](docs/extending.md).
## Demo Paths
Start with the recruiting demo path when showing the current product shape.
The inherited deal dashboard screenshots are retained as compatibility demos
for users who still rely on the staged cutover surface.
### 1. Recruit AI in-chat recruiting workflow
Use the bundled `recruiting_pipeline_demo` sample to ask:
```text
Which candidates best match this open position?
Which open positions best fit this candidate?
What client feedback changed the recommendation?
Generate a recruiting pipeline report.
```
The recruiting demo shows candidate-to-position and position-to-candidate
recommendations, visible fit dimensions, client-feedback adjustments, risk
flags, next questions, pipeline metrics, and Markdown/CSV report export.
### 2. Compatibility Deal Dashboard - MongoDB Atlas Charts
Active/Attention deal counts, pipeline value by stage, qualification health-band distribution, gap distribution, and open pipeline value - all on one screen. Each chart's aggregation pipeline is generated with the `render-atlas-dashboard` CLI and pasted into Atlas Charts (see the "Atlas Charts Dashboard" section below).
### 3. Compatibility Deal Dashboard - Claude / Codex
The compatibility deal surface takes raw MCP tool output and renders win rate,
the stage funnel, qualification gap patterns, data-quality coverage, and
attention items right inside the conversation. It starts from pasting in a
single meeting note - no extra app.
### Cost-aware LLM boundary
The MCP server should calculate, store, and retrieve structured deal data. The
host app - Claude Desktop, Codex, or ChatGPT - should usually explain that data
to the user.
Server-side LLM calls are reserved for workflows that create persistent
structured intelligence, especially `add_interaction`, `analyze_deal`, and
theme backfills. Read-only BI/review/reporting tools are designed to be
LLM-free so the host app can do the final narration without extra API cost.
> Company names and figures in these screens are all fictional demo data.
---
## Inherited Deal Health Compatibility
The inherited deal-intelligence tools use an active qualification framework to
turn messy customer evidence into comparable deal signals. MEDDPICC is the
bundled default B2B framework, and QF-v2 adds guarded custom framework support
so teams can copy a preset and adapt the criteria to their own sales motion.
You do not need to be a sales expert to use the compatibility tools; the
user-facing output is framed as health, missing information, risk signals,
customer themes, and next questions.
| Dimension | What it measures |
|---|---|
| **M**etrics | The quantified impact the customer expects (ROI, cost-reduction %) |
| **E**conomic Buyer | Who actually holds budget authority |
| **D**ecision Criteria | What the vendor-selection criteria are |
| **D**ecision Process | How the internal approval process runs |
| **I**dentify Pain | The customer's core problem and its urgency |
| **C**hampion | Whether you have an internal advocate |
| **C**ompetition | How you compare with competitors and the status quo |
When you add customer evidence through inherited deal tools, the server-side
LLM extracts these signals and stores the structured result. In `full` mode,
compatibility deal data persists in MongoDB Atlas and powers pattern analysis.
In optional `sample` mode, the same read/review surfaces run against bundled
fictional data so AI agents can evaluate the tool without setup.
---
## Product / solution context
Customer evidence and product knowledge are intentionally separate.
- Customer evidence is what prospects said in meetings, emails, interviews, or
calls. It can affect qualification, customer themes, and deal health.
- Product context is seller-side knowledge: your ICP notes, product facts,
pricing/packaging notes, security posture, integrations, differentiators,
competitive notes, or disqualifiers. It helps the extraction prompt interpret
customer evidence, but it is not counted as customer-stated evidence.
There are two normal ways to add product context.
1. Put files in a folder and tell the server where that folder is:
```text
Use update_config(product_context_source_dirs="C:\path\to\product-docs")
Then run index_product_context(dry_run=true)
If the preview looks right, run index_product_context(dry_run=false)
Finally, run get_product_context(query="security posture for healthcare")
```
2. Paste product/solution text into the host app and save it as a managed note:
```text
Use add_product_context_note(title="Healthcare security positioning",
content="...", dry_run=true)
If the preview looks right, call it again with dry_run=false and
confirmed_by_user=true.
Then run index_product_context and verify with get_product_context.
```
The first parser set supports `txt`, `md`, `json`, `csv`, `pdf`, and `docx`.
Presentation and spreadsheet files (`pptx`, `xlsx`) currently return warnings
and are planned for a later parser pass.
Product context is stored and cached locally under
`~/.recruit-ai/product-context` by default. Tool responses return snippets and
source metadata, not full raw documents. Large catalog PDFs are supported through
configurable source-file and chunk limits; check `index_product_context`
warnings for `partial_indexed` when a very large file was only partly indexed.
Files or pasted notes with
secret-shaped content are rejected or skipped.
---
## Product profiles
One repo, one package, three operating profiles:
| Profile | Use it for | Requires |
|---|---|---|
| `full` | Real team data on MongoDB Atlas | `MONGODB_URI`, plus ChatGPT OAuth or an API key for LLM tools |
| `sample` | Zero-config AI evaluation, demos, and lightweight local personal use | Python package only |
| `pro` | Paid-infra upgrade with Atlas Vector Search and API-key LLMs | Atlas M10+, `deal_summary_vector` index, `OPENAI_API_KEY` by default |
MongoDB Atlas URI quick path for `full`:
1. Create or sign in to MongoDB Atlas:
<https://www.mongodb.com/cloud/atlas/register>
2. Create a Free/M0 cluster.
3. Create a database user with read/write access.
4. Add your current IP address under Network Access.
5. Open Connect -> Drivers and copy the connection string.
6. Replace `<password>` locally and provide the URI through MCPB, `.env`, or
your shell environment. Do not paste the URI into chat or docs.
Start humans in `full`. Use `sample` only when the user explicitly wants a
zero-config trial, or when an AI agent needs to confirm the basic workflow
before asking for MongoDB. It begins with bundled fictional data; once you
create your own local deal, the bundled fixture is archived from the working
view and your local personal dataset becomes the active dataset. Some search
and LLM-heavy paths remain limited in sample mode. Move to `pro` only when paid
infrastructure is intentional.
`pro` defaults to `openai_api` with `gpt-5.4-mini` for lower API cost pressure.
You can still override `llm.openai_api_model` or switch `llm.provider` to
`anthropic` in user config.
MCP tools are profile-filtered by default:
- `sample`: zero-config/local personal tools
- `standard`: normal real-data tools
- `developer`: all registered tools, including demo seed/cleanup helpers
Use `get_tool_catalog` or `config_doctor` to inspect the current visible count
for your profile. Use `tools.surface: developer` or
`RECRUIT_AI_TOOLS_SURFACE=developer` only when you intentionally want the full
maintainer/debug surface.
---
## Fork And Customize
Fork this if your recruiting workflow is too specific for a generic ATS, but
too important to live only in resumes, spreadsheets, intake notes, and memory.
The repo is designed as a customizable MCP recruiting intelligence engine with
inherited deal-intelligence compatibility, not only a fixed demo app. Useful
fork paths include:
- solo recruiters or boutique search firms that need structured candidate and
client memory before adopting a heavier ATS;
- hiring teams that want transparent candidate-position fit criteria and
reusable client preference memory;
- MCP workflow builders experimenting with chat-first recruiting operations;
- teams that also want to preserve the inherited deal-intelligence workflows
for sales qualification, customer themes, or weekly pipeline reports.
Common extension seams:
- recruiting fit rubric dimensions and scoring criteria;
- candidate, client-company, position, and submission schemas;
- profile and tool-surface visibility;
- storage backends and MongoDB operational contracts;
- server-side LLM providers and cost tracking;
- report/export formats;
- product/solution context parsers;
- new MCP tools for team-specific workflows.
This project is MIT-licensed. You may use, copy, modify, merge, publish,
distribute, sublicense, and sell modified versions, subject to the license
terms. Keep the license and attribution notices when redistributing a fork.
For implementation entry points, read:
- [`docs/extending.md`](docs/extending.md) - extension seams and contracts;
- [`docs/customization-recipes.md`](docs/customization-recipes.md) - practical
fork recipes;
- [`docs/architecture.md`](docs/architecture.md) - module ownership, tool
ownership, and change playbooks.
---
## Install Overview
The normal product path is `full`: MongoDB Atlas-backed real recruiting/team
data, an MCP host, and one LLM credential path for extraction.
If you are a non-developer, the easiest route is to ask your AI assistant to
read [`AI_START_HERE.md`](AI_START_HERE.md). It will walk you through Python,
Node.js, MongoDB Atlas, and Claude/Codex/ChatGPT setup.
### Prerequisites
- Python 3.11+ in a conda environment
- Node.js 18+ for the no-git-clone `npx` bootstrapper
- One MCP chat client: Claude Desktop, or Codex/ChatGPT with MCP support
- For `full`: MongoDB Atlas account, Free/M0 cluster, and `MONGODB_URI`
- For LLM extraction/scoring: ChatGPT OAuth from a compatible subscription,
`ANTHROPIC_API_KEY`, or `OPENAI_API_KEY`
If you install Miniconda/Anaconda for the first time, the first `conda create`
or `conda install` may ask you to accept Anaconda Terms of Service for the
default package channels, commonly `https://repo.anaconda.com/pkgs/main` and
`https://repo.anaconda.com/pkgs/r`. Read the prompt and enter `a` to accept, or
run `conda tos accept` from Terminal/PowerShell after reviewing the terms. See
Anaconda's command-line ToS guide:
<https://www.anaconda.com/docs/getting-started/tos-plugin>.
Short version:
```text
Install Python and Node.js, prepare a MongoDB Atlas M0 connection string,
connect an MCP host, and choose one LLM path. Then use npx or an editable
Python install.
```
### npx install path
After the `recruit-ai-mcp@0.1.0` npm/PyPI packages are published and public
fresh-smoke evidence is recorded, most users should start here:
```bash
npx recruit-ai-mcp setup --python /path/to/python
npx recruit-ai-mcp mcp-config
```
Use the Python path printed by `mcp-config` in Claude Desktop MCPB. Set
`MONGODB_URI` through the MCPB install form, `.env`, or your shell environment.
On Windows, use `npx.cmd` if PowerShell blocks `npx`. Until the public registry
publication is complete, use the git clone/customization path below or the
maintainer local-wheel smoke path in `docs/bootstrapper-fresh-smoke.md`.
Detailed install guides:
- [`AI_START_HERE.md`](AI_START_HERE.md) - canonical AI-agent setup path,
including npx, editable install, full/sample choice, and first checks
- [`AI_FULL_INSTALL_GUIDE.md`](AI_FULL_INSTALL_GUIDE.md) - longer full-mode
walkthrough for non-developer users
- [`mcpb/README.md`](mcpb/README.md) - Claude Desktop MCP bundle
### Git clone / customization path
Clone or download this repository when you want to inspect or modify prompts,
reports, storage, qualification frameworks, or MCP tools. From the repository
root:
```bash
~/miniconda3/envs/deal-intel/python.exe -m pip install -e ".[embedding]"
```
Replace the Python path with the interpreter where you want the package
installed. Adding `[embedding]` installs local semantic-search dependencies.
After install, check the effective config:
```bash
recruit-ai config profiles
recruit-ai config show
```
### Readiness check
Run these before troubleshooting deeper issues:
```bash
recruit-ai config doctor --offline
recruit-ai smoke-profile --profile full --offline
```
When network access to Atlas is available, run a live storage ping:
```bash
recruit-ai storage-status
```
Use `config_doctor` from the MCP host after installing the bundle. It is the
first recovery tool when paths, profiles, MongoDB, or LLM readiness are unclear.
When `config_doctor` is OK, add the first recruiting records before expecting
meaningful recommendations: create a client company, create a position, create
a candidate, add recruiting evidence with `add_recruiting_interaction`, then
run `recommend_candidates_for_position`. Inherited deal-intelligence workflows
are still available for compatibility; for those, create or select a deal,
store customer evidence with `add_interaction`, then run `get_deal_review`.
### Optional zero-config smoke
```bash
$env:RECRUIT_AI_STORAGE_BACKEND='local_sample'
recruit-ai smoke-profile --profile sample
recruit-ai storage-status
recruit-ai smoke-natural-questions --pack recruiting --as-of 2026-06-22
recruit-ai smoke-natural-questions --as-of 2026-06-10
```
Use this only for zero-config evaluation. It starts with bundled fictional data
and does not require MongoDB, paid APIs, or Atlas Vector Search.
`full` starts with the user's own MongoDB data and does not auto-seed sample
records. If you want a richer Atlas-backed demo, switch to the `developer` tool
surface and run `create_sample_data`; it can write either
`weekly_pipeline_demo` deal records or `recruiting_pipeline_demo` recruiting
records to the configured demo database, not to the primary real-data database.
---
## Zero-config sample mode (no MongoDB)
If you only want to test the BI and deal-review flows, you can run the bundled
fictional sample dataset without MongoDB Atlas, API keys, or Atlas Vector
Search.
Temporary PowerShell session:
```powershell
$env:RECRUIT_AI_STORAGE_BACKEND='local_sample'
& "$HOME\miniconda3\envs\deal-intel\python.exe" -m deal_intel.cli storage-status
& "$HOME\miniconda3\envs\deal-intel\python.exe" -m deal_intel.cli smoke-natural-questions --pack recruiting --as-of 2026-06-22
& "$HOME\miniconda3\envs\deal-intel\python.exe" -m deal_intel.cli smoke-natural-questions --as-of 2026-06-10
```
Persistent sample profile:
```bash
recruit-ai config init --profile sample --dry-run
recruit-ai config init --profile sample
```
Sample mode is intentionally limited, but it is no longer purely read-only.
Core dashboard, reporting, customer-theme, deal-review, create/update/stage,
interaction-ingestion, and lifecycle flows can run against local personal data.
`add_interaction` requires a ready LLM provider and works on user-created local
deals; use `interaction_type: meeting` for meeting notes. Local sample mode
skips embedding storage and does not expose raw content in list/BI/report
paths. Semantic `search_deals`, Atlas Charts, and shared team operation still
belong to MongoDB-backed `full` or `pro` mode.
Local personal data defaults to `~/.recruit-ai/local-data` and can be changed
with `storage.local_data_dir`.
Useful local-data commands:
```bash
recruit-ai local-data status
recruit-ai local-data export
recruit-ai local-data reset # dry-run
recruit-ai local-data reset --force # clears local deals/recruiting, preserves audit logs
recruit-ai local-data migrate-to-mongo # dry-run
recruit-ai local-data migrate-to-mongo --apply # writes local records to MongoDB
```
The bundled fictional deal fixture is immutable. After local personal deals
exist, the fixture is hidden from active deal reads instead of being mixed with
your data. Zero-config recruiting records are stored in local
`recruiting.json`, so you can create a client, role, candidate, feedback, and
first recommendation without MongoDB. The dry-run-first local-to-MongoDB
migration command migrates user-created local personal deals and recruiting
records, never bundled fixture records or local delete audit logs. If no local
personal records exist yet, the dry-run returns immediately and skips MongoDB
target readiness checks.
---
## Tool guide
The detailed guide below focuses on the core user-facing workflow. For the
complete current tool contract, read [`docs/baseline.md`](docs/baseline.md).
> **Tip**: In Claude Desktop, type the example sentences below verbatim or say
> something similar. For recruiting workflows, start with a client company,
> position, and candidate before asking for recommendations.
### Recruiting-first workflow
The current recruit-ai surface supports an evidence-first recruiting loop while
the inherited deal-intelligence tools remain available for compatibility.
Use this sequence for a new search:
1. `create_client_company` - create the hiring customer.
2. `create_position` - create the open role or search mandate.
3. `create_candidate` - add candidate profiles.
4. `add_recruiting_interaction` - add screens, client intake notes, interviews,
email threads, call summaries, or internal notes.
5. `add_client_feedback` - capture client response and reusable preference
learning.
6. `recommend_candidates_for_position` or `recommend_positions_for_candidate`
- rank fit in either direction.
7. `get_recruiting_metrics` - inspect funnel, status, feedback, and data
quality.
8. `export_recruiting_report` - write local Markdown and CSV pipeline artifacts.
These recruiting tools are deterministic today. They do not call LLMs,
embeddings, or Atlas Vector Search, and they work on Atlas M0 with the existing
Python ranking path.
#### `create_client_company`
**When to use**: Before creating positions for a hiring customer or saving
client preference memory.
**Example**:
```text
Create client company Northstar Health. Industry healthcare technology,
stage growth, locations Boston and Remote US. Hiring preferences: values
healthcare data platform experience and written architecture rationale.
```
Useful fields include `name`, `industry`, `stage`, `locations`,
`hiring_preferences`, and `risk_notes`.
#### `create_position`
**When to use**: To define the role/search mandate the recruiter is trying to
fill.
**Example**:
```text
Create a position for Northstar Health: Senior Backend Platform Engineer.
Must have Python, data platforms, and healthcare. Nice to have Kafka and HIPAA.
Target compensation 180k to 230k USD. Remote-friendly, Boston or Remote US.
```
The position stores role requirements, target compensation, location/remote
constraints, and the default recruiting fit rubric:
`skill_fit`, `domain_fit`, `seniority_fit`, `compensation_fit`,
`location_fit`, `availability_fit`, `client_preference_fit`, and `risk`.
#### `create_candidate`
**When to use**: To add or update a candidate profile before matching.
**Example**:
```text
Create candidate Avery Chen. Current title Staff Backend Engineer at Clearpath
Systems. Skills Python, FastAPI, PostgreSQL, Kafka, HIPAA. Domains healthcare
and data platforms. Seniority staff. Locations Boston and Remote US.
Availability 30 days.
```
Candidate records store skills, domains, seniority, locations, work
authorization, availability, compensation expectation when known, preferences,
risk flags, and evidence references.
#### `add_recruiting_interaction`
**When to use**: To attach evidence to a candidate, client company, position,
or submission.
**Example**:
```text
Add a candidate_screen interaction for cand_avery_chen. Summary: Avery wants
staff-level backend platform work, is strongest in healthcare data workflows,
and can start in about 30 days.
```
`raw_content` may be stored, but normal responses hide it. This keeps evidence
available for future internal workflows without leaking raw notes in ordinary
tool output.
#### `add_client_feedback`
**When to use**: After a client, hiring manager, candidate, or recruiter gives
feedback on a submission, candidate, or position.
**Example**:
```text
Add positive feedback for submission sub_avery_northstar_backend. Decision
signal advance. Summary: hiring manager liked the healthcare platform depth.
Rubric deltas: domain_fit +1 and client_preference_fit +1.
```
Feedback can include `rubric_deltas` so repeated client preference patterns
adjust future fit scoring transparently. If feedback is attached to a
submission, the service links the feedback id back to that submission when the
submission exists.
#### Recommendations
Use `recommend_candidates_for_position(position_id=...)` when a client asks
"who should we show for this role?" Use
`recommend_positions_for_candidate(candidate_id=...)` when a candidate asks
"which open searches fit me?"
Both tools:
- use M0-safe lexical retrieval as a prefilter;
- score final fit with the deterministic recruiting rubric;
- include reasons, low-fit rejection notes, risk flags, and next questions;
- preview by default;
- write a `recommendation_runs` record only when `save_run=true`.
#### Recruiting metrics and report export
Use `get_recruiting_metrics` for chat answers about open positions,
submissions, placements, feedback rates, and data quality. Use
`export_recruiting_report` when you need local Markdown/CSV artifacts for a
pipeline review.
Example:
```text
Show recruiting pipeline metrics.
Export the recruiting pipeline report for today.
```
#### Recruiting demo data
For an Atlas-backed fictional recruiting demo, switch to the `developer`
surface and run:
```text
create_sample_data(dataset="recruiting_pipeline_demo")
```
The first call should stay dry-run. Real writes require `dry_run=false` and
`confirmed_by_user=true`, and they go to the configured demo database, not the
primary real-data database.
### Inherited deal workflow
The remaining guide covers the inherited deal-intelligence workflow that still
ships during the staged recruiting cutover. Use it only when the user is
working with sales/deal records rather than recruiting candidates and roles.
You can find a `deal_id` with `create_deal` or `list_deals`.
---
### 1. `create_deal` - create a new deal
**When to use**: Run this first when you start engaging a new prospect.
**Example**:
```
Create a new deal for Hyundai Precision. Manufacturing industry, deal size 200M KRW.
```
**Parameters**:
| Parameter | Required | Description |
|---|---|---|
| `company` | required | Customer company name |
| `industry` | optional | True business vertical (e.g., "Manufacturing", "Finance", "Retail") |
| `industry_tags` | optional | Additional vertical tags for cross-industry accounts. The primary `industry` is automatically included |
| `customer_segment` | optional | Customer segment or maturity label (e.g., "startup", "enterprise", "public_sector", "Series B", "Pre-IPO") |
| `deal_size_amount` | optional | Median expected contract size in `deal_size_currency` units (e.g., 200000000) |
| `deal_size_currency` | optional | ISO-style 3-letter currency code. Defaults to `deal_value.default_currency` (`KRW` by default) |
| `deal_size_status` | required when an amount is given | Amount status: `unknown`, `rough_estimate`, `customer_budget`, `quoted`, `strategic_zero` |
| `deal_size_low_amount` / `deal_size_high_amount` | optional | Estimate range. Omitted -> treated as equal to the median in metrics |
| `deal_size_note` | optional | Rationale for the amount classification, or a user memo |
| `expected_close_date` | optional | Expected close date. Omitted -> config default applies |
**Example result**:
```json
{
"ok": true,
"deal_id": "a3f9...",
"company": "Hyundai Precision",
"industry": "Manufacturing",
"industry_tags": ["Manufacturing"],
"customer_segment": "enterprise",
"deal_size_amount": 200000000,
"deal_size_currency": "KRW",
"deal_size_status": "rough_estimate",
"expected_close_date": "2026-06-15",
"expected_close_date_source": "config_default"
}
```
Remember this `deal_id`, or look it up later with `list_deals`.
If you omit the expected close date, a default of 7 days after creation is filled in. This is an operational default, not a confirmed schedule. A date you provide always takes precedence over config.
When you enter a deal amount, you must also set its status. If unknown, leave `deal_size_status="unknown"` and leave the amount blank. If only a positive amount is given, the tool asks which basis applies - sales estimate / customer budget / quote sent. If only 0 is given, it doesn't save immediately and asks whether it's a strategic free/reference deal or an undecided amount. If undecided, it's saved as `unknown` with the amount blanked. An intentional zero-value deal (free sample, reference win) is saved with `deal_size_amount=0` and `deal_size_status="strategic_zero"`. If you heard a customer budget or sent a quote, use `customer_budget` or `quoted` so it counts as a validated pipeline value in metrics. Mixed currencies are not silently summed; metric and report outputs expose currency fields or per-currency breakdowns.
```yaml
deal_value:
default_currency: KRW
pipeline:
expected_close:
default_days: 7
days_by_segment:
public_sector: 60
enterprise: 28
days_by_industry:
Government: 60
Manufacturing: 28
reporting:
timezone: Asia/Seoul
```
Keep `industry` as the single primary business vertical. If an account is
cross-industry, put the other verticals in `industry_tags`; the primary
`industry` is always included in that tag list. Put account maturity,
ownership, buying segment, or funding stage in `customer_segment` instead.
Industry input is normalized against the built-in taxonomy when possible, so
values such as `제조`, `핀테크`, or `보험·금융·대기업` are stored as canonical
metadata such as `industry=Insurance`, `industry_tags=["Insurance", "Finance"]`,
and `customer_segment=enterprise`. Segment overrides apply first; industry
overrides apply second, both on a case-insensitive exact match. Auto-dates use
the business date in the reporting timezone, while stored audit timestamps stay
in UTC.
For existing data, use the taxonomy cleanup CLIs:
```bash
recruit-ai audit-taxonomy
recruit-ai apply-taxonomy-cleanup
recruit-ai apply-taxonomy-cleanup --apply --confirmed-by-user
recruit-ai backfill-industry-tags
recruit-ai backfill-industry-tags --apply --confirmed-by-user
```
`audit-taxonomy` is read-only. `apply-taxonomy-cleanup` and
`backfill-industry-tags` are dry-run by default. They automatically normalize
recognizable mixed labels into primary industry, industry tags, and customer
segment. If an industry is missing, the tool treats it as an enrichment task:
it either drafts a medium-confidence industry from the company name or returns a
web research query so the AI client can look it up and call `update_deal`. The
default UX is draft-first and correction-friendly; only impossible rows stay out
of writes.
---
### 2. `add_interaction` - add a customer interaction
**When to use**: Right after a customer meeting, email reply, user interview, call summary, or internal note. Paste the content as-is and the server-side LLM extracts active-framework qualification signals and customer themes with source-aware scoring. MEDDPICC is the default built-in framework.
Cost note: this is intentionally one of the few places where the MCP server
uses its own LLM provider, because the extracted result is persisted as product
data. For explanation-only questions, prefer read tools such as
`get_deal_review`, `get_deal_gaps`, and `get_metrics`; let Claude/Codex explain
their deterministic output.
Meeting notes are just `interaction_type: meeting`. Older `meetings` records
are still read as legacy fallback, but new integrations should write canonical
`interactions` records through this tool.
**Example**:
```
Add today's (2026-06-08) meeting note to Hyundai Precision, deal_id: a3f9...
Use interaction_type=meeting and direction=inbound.
Notes:
Met Director Kim (purchasing decision-maker). Current production-line defect rate
is 3.2%, causing ~1.5B KRW/yr loss. Our solution targets <=1.5%. Manager Park is in
favor internally. Competitor A is under review but costs 2x. Internal approval due
end of June.
```
**Parameters**:
| Parameter | Required | Description |
|---|---|---|
| `deal_id` | required | Target deal ID |
| `date` | required | Interaction date (YYYY-MM-DD) |
| `interaction_type` | required | `meeting`, `email_thread`, `user_interview`, `call_summary`, `internal_note`, or a configured custom type |
| `direction` | required | `inbound`, `outbound`, `mixed`, or `internal` |
| `content` | required | Raw interaction content (Korean or English both fine) |
| `participants` | optional | Names/emails/roles if known |
| `subject` | optional | Email/call/meeting subject |
| `source_confidence` | optional | Override source confidence when needed |
| `allow_duplicate` | optional | Defaults to `false`; set to `true` only when repeating identical content is intentional |
Safety and cost guardrails:
- `content` is capped at 20,000 characters before any LLM call.
- Identical same-day, same-type, same-direction content is skipped before any
LLM call unless `allow_duplicate=true`.
- Interaction content and seller-side product context are treated as untrusted
source text; embedded instructions inside those sources are ignored.
**What the result includes**:
- `qualification` - active-framework scores + evidence extracted from this interaction
- `qualification_latest` - the deal's cumulative health_pct + per-dimension trend
- `meddpicc` / `meddpicc_latest` - compatibility aliases used when the active framework is MEDDPICC or legacy records are read
- `summary` - a 2-3 sentence LLM-generated summary
- `customer_themes` - customer concerns / selection criteria extracted from this interaction
- `scoring_applied` - whether this source updated qualification health/customer themes
- `source_policy` - why this source was treated as confirmed evidence or stored as unconfirmed context
- `stage_suggestion` - filled only when the content explicitly implies a stage transition (e.g., contract signed -> won, lost deal -> lost); otherwise `null`
- `embedding_stored` - whether the similar-deal-search embedding was stored
Source-aware scoring is deliberately conservative:
- `direction=inbound` defaults to `source_confidence=customer_stated`, so explicit customer replies, interviews, and meeting notes can update qualification/customer themes.
- `direction=outbound` defaults to `source_confidence=outbound_unconfirmed`, so seller-only emails are stored but do not improve health scores.
- `interaction_type=internal_note` or `direction=internal` defaults to `source_confidence=internal`, so internal hypotheses stay out of confirmed scoring.
- `direction=mixed` is allowed for email threads or calls with both sides represented; only explicit customer statements should be treated as evidence.
> **The stage never changes automatically.** Even if the content says "contract signed," `add_interaction` does not change the stage directly - it only **suggests** via `stage_suggestion`. When Claude asks "shall I move this deal to won?", `update_stage` makes the actual change after you confirm. This is a deliberate separation to prevent wrong auto-closing.
---
### 3. `get_deal` - view deal details
**When to use**: To check a specific deal's safe history, qualification scores, and interaction records.
**Example**:
```
Show me the full Hyundai Precision deal. deal_id is a3f9...
```
You get stored interactions, any legacy meeting records, per-interaction
qualification extraction, and the cumulative health_pct. Legacy
`meddpicc_latest` fields are still returned for compatibility when present.
Raw meeting notes, raw interaction content, contacts, and embeddings are
excluded from this normal read path.
Developer surface only: `get_deal_raw` can intentionally return raw notes,
raw interaction content, and contacts for admin/debug inspection, but it
requires `confirmed_by_user=true`, a non-empty `reason`, and
`include_raw_content=true`. Embeddings are still excluded.
---
### 4. `update_stage` - change the pipeline stage
**When to use**: When a deal moves to the next stage or the outcome is finalized.
```text
update_stage(deal_id, new_stage, actual_close_date="")
```
**Example**:
```
Move the Hyundai Precision deal to the proposal stage.
```
When moving to `won` or `lost`, you can specify the actual close date as `YYYY-MM-DD`. Omitted -> the processing day is stored. `expected_close_date` stays as the forecast, and `stage_history.entered_at` is the system audit time, kept distinct from the actual close date. Moving a closed deal back to an open stage clears `actual_close_date`.
**Stages** (in order):
```
discovery -> qualification -> proposal -> negotiation -> won / lost / stalled
```
**What the result includes**:
- `actual_close_date` - the real close date for Won/Lost
- `days_in_previous_stage` - how long it spent in the previous stage
- `stuck_threshold_days` - the stuck threshold for a new Active stage; otherwise `null`
- Qualification gaps are recomputed per stage (e.g., under the default MEDDPICC framework, a drop in Identify Pain at the proposal stage is not a gap - it's a positive signal that the pain is being resolved)
---
### 5. `update_deal` - fix an existing deal's amount or confirmed metadata
**When to use**: When an existing deal's `deal_size_status` is missing, to save after the user confirms customer-budget / quote / strategic-zero, or to correct confirmed metadata such as company, industry, industry tags, customer segment, and close dates.
This tool stays intentionally narrow. It can update deal-value fields and selected metadata, but it does not change pipeline stage, interactions, meetings, contacts, or raw notes. Stage transitions still go through `update_stage`.
**Example**:
```
The existing ArcanaGames deal has evidence of a closed contract, so save it as quoted.
Note the rationale as "CEO said let's sign today and paid same-day."
```
**Required conditions**:
- `confirmed_by_user=true`
- Value updates require `deal_size_note` with the user's confirmation rationale or meeting evidence
- Metadata updates require `update_note` or a fallback `deal_size_note`
Value edits are logged to `deal_value_history`; metadata edits are logged to
`deal_metadata_history`. Recognizable mixed industry labels are normalized into
primary industry, `industry_tags`, and `customer_segment`; unmapped labels should
be corrected with an explicit confirmed update.
---
### 6. `list_deals` - see all deals at a glance
**When to use**: When you want the whole pipeline at a glance. Good for a weekly review.
**Example**:
```
Show me all deals, stuck ones first.
```
Or a specific stage only:
```
Show me only the deals in the proposal stage.
```
**Result**:
- `health_pct` - overall qualification score (0-100; MEDDPICC by default)
- `gaps` - list of weak, low-scoring dimensions
- `is_stuck` - whether the Active-stage dwell time exceeds the per-stage threshold
- `is_overdue` / `overdue_days` - whether an Open deal passed its expected close date
- `attention_reasons` - multiple reasons: `stalled`, `overdue`, `stuck`, `at_risk`
- `days_in_stage` - days spent in the current stage
- `data_quality` - per-deal missing/invalid/estimated fields and overall coverage
- `as_of`, `timezone`, `generated_at` - reporting base date and generation time
Specify `as_of="YYYY-MM-DD"` to re-run date-based calculations against the same base date. Stuck deals sort to the top.
---
### 7. `analyze_deal` - optional generated BD strategy
**When to use**: Only when you explicitly want the server-side LLM to draft a BD strategy memo. By default, this is preview-only and does not persist `bd_strategy` back onto the deal.
For routine deal status, risk, uncertainty, and next-question review, prefer
`get_deal_review`. For "what information are we missing?" use
`get_deal_gaps`. Those read paths are deterministic, LLM-free, and cheaper.
**Example**:
```
Generate a BD strategy memo for the Hyundai Precision deal.
```
The result includes:
- a summary of current qualification health
- concrete responses per weak dimension
- a recommended agenda for the next meeting
When product context has been indexed, `analyze_deal` may use bounded
seller-side snippets to improve product-fit and positioning advice. Same
deal/prompt/product-context calls are cached for 10 minutes to avoid repeated
LLM spend; `force=true` bypasses the cache. To persist the generated strategy,
call with `persist_strategy=true` and `confirmed_by_user=true`. Confirmed
persistence stores product-context reference metadata with the generated
strategy, not raw product documents. Preview usage is returned in the response
only and is not counted by persisted usage reports.
---
### 8. `get_metrics` - current pipeline-health KPIs
**When to use**: For instant BI questions in Claude/Codex like "how's pipeline health right now'", "how many at-risk deals'", "show pipeline value and health by stage."
This is the default read tool for numeric pipeline answers. Do not use
`list_deals` to hand-calculate KPIs, and do not use `get_insights` unless the
question is about a legacy/special BI pattern such as win/loss comparison or
stage velocity.
Supported metric types are `pipeline_health` and `pipeline_trend`.
**Parameters**:
| Parameter | Required | Description |
|---|---|---|
| `metric_type` | optional | `pipeline_health` or `pipeline_trend` |
| `stage` | optional | Exact match against the stored stage |
| `industry` | optional | Exact match against the stored industry |
| `as_of` | optional | Base date for stuck/overdue calculation, `YYYY-MM-DD` |
**What the result includes**:
- `kpis`: active/open/stalled/terminal count, open value, avg health, coverage, stuck/overdue, attention count
- `stage_breakdown`: count/value/health/stuck/overdue in canonical stage order
- `health_bands`: healthy/watch/at_risk/unassessed counts
- `attention_reasons`: stalled/overdue/stuck/at_risk reason counts and unique attention-deal count
- `pipeline_values`, `win_rate`, `data_quality`, `warnings`
The BI path uses no LLM and no embedding. Raw notes, contacts, and vectors are excluded from the metric read path.
**Example**:
```
Tell me the current pipeline health
```
```
Show pipeline health for the proposal stage only
```
```
Show stuck/overdue status for IT-industry deals
```
---
### 9. `get_deal_gaps` - surface the customer intel you're still missing
**When to use**: When you want to know what you still need to confirm before pursuing, forecasting, or reviewing a deal.
This is not a table-completeness checker. It prioritizes missing or weak information by practical sales impact and forecast trust. It is read-only, uses no LLM, uses no embedding, and excludes raw notes, raw interaction content, contacts, and vectors.
Use this for missing-information questions across the pipeline or for a single
deal. Use `get_deal_review` when the user wants a broader one-deal status/risk
review.
**Parameters**:
| Parameter | Required | Description |
|---|---|---|
| `as_of` | optional | Base date for stuck/overdue calculation, `YYYY-MM-DD` |
| `stage` | optional | Exact match against the stored stage |
| `industry` | optional | Exact match against the stored industry |
| `deal_id` | optional | Exact deal id. Returns that deal regardless of `min_priority` and `limit` |
| `min_priority` | optional | `low`, `medium`, or `high`. Defaults to `medium` |
| `limit` | optional | 1 to 50. Defaults to 10 |
**What the result includes**:
- `summary`: deal count, gap-deal count, priority counts, gap status/type counts
- `deals`: prioritized deals with health band, attention reasons, priority score, and gaps
- each gap includes reason, suggested question, and recommended action
**Example**:
```
What important customer information are we missing for active deals'
```
```
Show high-priority gaps for negotiation deals
```
```
For this deal_id, what should I confirm next'
```
---
### 10. `export_report` - generate a pipeline report
**When to use**: When you need a file to share or for a meeting, like "make this week's pipeline report."
Use `export_report` for manager/team meeting reports and narrative pipeline
briefings. For chat-only KPI answers, use `get_metrics` instead. For
spreadsheet-ready CSV ledgers, use `export_data` instead.
**Parameters**:
| Parameter | Required | Description |
|---|---|---|
| `report_type` | optional | `weekly_pipeline` or `pipeline_trend`; defaults to `weekly_pipeline` |
| `output_dir` | optional | Save path. Omitted -> `reporting.output_dir` or `~/.recruit-ai/reports`; relative paths are scoped under `~/.recruit-ai/` |
| `stage` | optional | Exact match against the stored stage |
| `industry` | optional | Exact match against the stored industry |
| `as_of` | optional | Base date for stuck/overdue calculation, `YYYY-MM-DD` |
**What the result includes**:
- `csv_path`, `markdown_path`: absolute paths of the generated files
- `artifacts`: report artifact filename, path, encoding
- `metrics`, `warnings`, `row_count`
- `briefing`, `briefing_sections`: compact meeting-ready summary text
- `host_report_prompt`: a safe prompt the host app can use to polish the
deterministic data pack into a more natural manager/team report
The deterministic report data pack uses no LLM and no embedding. Host apps may
use that data pack to produce more polished meeting prose, but should not
change numbers, company names, stages, amounts, health scores, or warning codes.
**Example**:
```
Make this week's pipeline report
```
```
Export the proposal stage only as a weekly pipeline report
```
---
### 11. `export_data` - export Excel/CSV-ready deal ledgers
**When to use**: When the user asks for raw-but-safe CSV data, Excel records,
open deal tables, all-deal ledgers, or won/lost postmortem rows.
Use `export_data` for spreadsheet work. It is not a narrative report tool.
**Datasets**:
| Dataset | Purpose |
|---|---|
| `open_deals` | Active/stalled pipeline ledger with health, timing, attention, gaps, pain, and decision criteria |
| `all_deals` | Full safe deal ledger for filtering and recordkeeping |
| `closed_deals` | Won/lost ledger with close metadata and postmortem fields |
**Parameters**:
| Parameter | Required | Description |
|---|---|---|
| `dataset` | optional | `open_deals`, `all_deals`, or `closed_deals`; defaults to `open_deals` |
| `output_dir` | optional | Save path. Omitted -> `reporting.data_output_dir`, `reporting.output_dir`, or `~/.recruit-ai/reports`; relative paths are scoped under `~/.recruit-ai/` |
| `stage` | optional | Exact match against the stored stage |
| `industry` | optional | Exact match against the stored primary industry |
| `as_of` | optional | Base date for stuck/overdue calculation, `YYYY-MM-DD` |
`export_data` excludes raw notes, raw email/interview/call content, contacts,
and embeddings. It writes UTF-8 BOM CSV and guards spreadsheet formula
injection.
**Example**:
```
Export the open deal ledger as CSV
```
```
Create a won/lost CSV for postmortem review
```
---
### 12. `get_usage` - inspect server-side LLM usage
**When to use**: When you want to know how much server-side LLM work this MCP
has performed, such as token counts, call counts, and safe cost estimates.
This is read-only. It never returns prompts, raw notes, raw emails, API keys,
OAuth tokens, or MongoDB URIs. ChatGPT OAuth is shown as subscription-backed
with zero incremental API estimate. API-provider costs are estimated only when
you configure `usage.pricing`.
**Example**:
```
Show my Recruit AI MCP usage this month.
```
```
Show usage since 2026-06-01.
```
---
### Atlas Charts Dashboard - `Weekly Pipeline Review`
When you'd rather see it on screen than as CSV/Markdown, use the Atlas Charts dashboard. The dashboard aggregation spec and setup runbook are in [`docs/atlas-charts.md`](docs/atlas-charts.md).
Render command:
```bash
~/miniconda3/envs/deal-intel/python.exe -m deal_intel.cli render-atlas-dashboard --as-of 2026-06-09 --output outputs/atlas_charts/weekly_pipeline_review_20260609.json
```
To paste a single chart into the Atlas Query bar:
```bash
~/miniconda3/envs/deal-intel/python.exe -m deal_intel.cli render-atlas-dashboard --as-of 2026-06-09 --chart-id pipeline_kpis
```
The six managed chart ids are `pipeline_kpis`, `stage_breakdown`, `health_bands`, `attention_deals`, `qualification_gap_distribution`, and legacy-compatible `meddpicc_gap_distribution`.
Cross-check the dashboard numbers:
```bash
~/miniconda3/envs/deal-intel/python.exe -m deal_intel.cli crosscheck-weekly-dashboard --as-of 2026-06-09 --output-dir outputs/m3_3_crosscheck
```
---
### 13. `get_insights` - legacy/special BI analysis
**When to use**: To aggregate all deal data and spot patterns. Good for monthly reviews and learning win/loss patterns.
Prefer `get_metrics` for current pipeline-health KPIs. Prefer customer theme
tools for customer concerns, decision criteria, and evidence. `get_insights`
remains useful for special BI variants such as win/loss comparison, gap
frequency, industry benchmark, and stage velocity.
You can specify `as_of`; the response includes `timezone` and a UTC `generated_at`. These label the current collection snapshot - they don't reconstruct historical document state.
**Seven analysis types**:
| query_type | What it tells you |
|---|---|
| `pipeline_overview` | Deal count / avg health / total size by stage |
| `win_patterns` | Average legacy/default-framework scores of Won deals |
| `loss_patterns` | Average legacy/default-framework scores of Lost deals |
| `compare_won_lost` | Per-dimension score gap between Won and Lost |
| `gap_frequency` | The dimensions most often missing in active deals |
| `industry_benchmark` | Avg health / win rate / deal size by industry |
| `stage_velocity` | Average dwell days per stage |
**Example**:
```
Show me the whole pipeline overview.
```
```
What's the qualification pattern difference between deals we win and deals we lose'
```
```
Which dimension is most often missing'
```
---
### 14. `search_deals` - semantic similar-deal search
**When to use**: When you want to reference how past deals in similar situations played out. Search in natural language.
Do not use semantic search for frequency/ranking questions such as "what do
customers worry about most?" Use `get_customer_themes` for that. `search_deals`
is for similar-case retrieval in Mongo-backed mode.
**Example**:
```
Find deals where the customer struggled with cost reduction.
```
```
Show deals with a strong champion and a clear decision structure.
```
```
Any deals with a pattern similar to Hyundai Precision'
```
**How it works**:
1. Convert the query into a 384-dim vector
2. Compute cosine similarity against every deal's meeting-summary vector
3. Return them sorted by similarity, highest first
**What the result includes**:
- `score` - similarity (0-1, higher = more similar)
- `deal_stage`, `health_pct`, `gaps` - the deal's current state
> The local embedding model warms up in the background at server start. While it's loading, `warming_up: true` is returned, so retry after 5 seconds. After 30+ seconds it switches to a stalled error.
---
### 15. `get_customer_themes` - frequency of customer concerns / selection criteria
**When to use**: To group meeting evidence across deals and see the topics customers worry about most. It counts by unique deal (not by meeting) and returns representative companies and evidence.
Customer themes are intentionally a 3-step workflow:
1. `get_customer_themes` ranks recurring concerns or decision criteria.
2. `get_customer_theme_breakdown` compares those themes by stage, primary
industry, industry tag, or theme dimension.
3. `get_customer_theme_evidence` shows privacy-safe snippets for one known
`theme_key`.
For "show me examples" follow up with `get_customer_theme_evidence`. For
stage/industry/tag comparison, use `get_customer_theme_breakdown`.
**Example**:
```
Show the top 5 things customers worried about most across active deals.
```
```
Tell me the most frequent themes and evidence in Decision Criteria.
```
**Filters**:
- `dimension`: `all`, `identify_pain`, `decision_criteria`, `metrics`
- `stage`: `active`, `all`, or an individual deal stage
- `industry`: primary industry or `industry_tags` match
- `top_k`: up to 20
For cross-industry accounts, keep pipeline and forecast metrics on the single
primary `industry`, then use Customer Themes with the `industry` filter or
`get_customer_theme_breakdown(group_by="industry_tag")` to see semantic
industry-tag groupings.
To backfill themes onto existing data, run this first:
```bash
~/miniconda3/envs/deal-intel/python.exe -m deal_intel.cli backfill-customer-themes --apply
```
`backfill-customer-themes` is a maintenance/migration command for historical
meeting records. It may call the configured server-side LLM once per processed
meeting, so run the dry-run first, consider `--limit`, and avoid treating it as
normal daily intake. New emails, interviews, calls, and meetings should go
through `add_interaction`.
The versioned Atlas Charts spec is in
`atlas/charts/customer_themes.v1.json`. See `docs/atlas-charts.md` for the
Customer Themes dashboard setup, including the optional
`pain_by_industry_tag` chart.
---
## Recommended workflow
Recruiting-first:
```
1. New client/search -> create_client_company + create_position
2. New candidate -> create_candidate
3. Evidence capture -> add_recruiting_interaction
4. Client preference -> add_client_feedback
5. Role-to-candidate match -> recommend_candidates_for_position
6. Candidate-to-role match -> recommend_positions_for_candidate
7. Pipeline KPIs -> get_recruiting_metrics
8. Local report artifacts -> export_recruiting_report
```
Inherited deal workflow:
```
1. Right after customer evidence -> add_interaction (meeting/email/interview/call)
2. On stage change -> update_stage
3. One-deal status/risk -> get_deal_review
4. Before pursuing/forecast -> get_deal_gaps (what's still missing)
5. Optional strategy memo -> analyze_deal (LLM-written BD strategy)
6. Weekly review -> list_deals (find stuck deals)
7. Pipeline KPIs -> get_metrics pipeline_health
8. Usage / cost check -> get_usage
9. Monthly retro -> get_insights compare_won_lost / stage_velocity
10. Reference similar cases -> search_deals
11. Customer-concern analysis -> get_customer_themes
12. Dashboard -> Atlas Charts Weekly Pipeline Review
```
---
## Architecture
Current source of truth:
- MCP server: `src/deal_intel/mcp_server.py`
- Current registered tool count: see `get_tool_catalog` or `config_doctor`
- Detailed contract: [`docs/baseline.md`](docs/baseline.md)
- Documentation map: [`docs/README.md`](docs/README.md)
- User memory samples: [`user_docs/README.md`](user_docs/README.md)
```
[Claude Desktop / Codex - natural-language input]
| stdio JSON-RPC
v
[recruit-ai-mcp FastMCP server]
|
|-- LLM Provider
| |-- ChatGPT OAuth (default, Plus/Pro subscription)
| |-- Anthropic API (optional)
| `-- OpenAI API (optional)
|
|-- Embedding Provider
| `-- sentence-transformers all-MiniLM-L6-v2
| -> runs locally / no API key / 384 dims
|
`-- Storage
|-- local_sample : bundled fixture + local deals/recruiting
`-- MongoDB Atlas : recruiting collections plus inherited deals
search_deals
|-- M0 default : reads summary_embedding, computes cosine in Python
`-- M10+ option : uses the Atlas Vector Search index
```
### Deal document schema (key fields)
```json
{
"deal_id": "uuid",
"company": "Hyundai Precision",
"industry": "Manufacturing",
"deal_size_amount": 200000000,
"deal_size_currency": "KRW",
"deal_stage": "proposal",
"expected_close_date": "2026-09-30",
"expected_close_date_source": "user_provided",
"actual_close_date": null,
"stage_history": [
{"stage": "discovery", "entered_at": "2026-05-01T..."},
{"stage": "qualification", "entered_at": "2026-05-15T..."},
{"stage": "proposal", "entered_at": "2026-06-01T..."}
],
"interactions": [
{
"interaction_id": "uuid",
"meeting_id": "uuid",
"date": "2026-06-08",
"interaction_type": "meeting",
"direction": "inbound",
"source_confidence": "customer_stated",
"raw_content": "Met Director Kim. Defect rate 3.2% -> target 1.5%...",
"summary": "2-3 sentence LLM-generated summary",
"meddpicc": {
"metrics": {"score": 4, "evidence": "~1.5B KRW/yr loss"},
"identify_pain": {"score": 5, "evidence": "defect rate 3.2%, line urgent"},
"champion": {"score": 3, "evidence": "Manager Park in favor"}
}
}
],
"meddpicc_latest": {
"health_pct": 72.4,
"gaps": ["economic_buyer", "decision_criteria"],
"metrics": {"score": 4.0, "trend": "up"},
"identify_pain": {"score": 5.0, "trend": "flat"},
"champion": {"score": 3.0, "trend": "up"}
},
"summary_embedding": [0.012, -0.034, ...],
"created_at": "2026-05-01T...",
"updated_at": "2026-06-08T..."
}
```
### Module structure
```
src/deal_intel/
mcp_server.py FastMCP entry point
cli.py typer CLI (login-chatgpt, backfill-customer-themes,
render-atlas-dashboard, crosscheck-weekly-dashboard,
smoke-deal-review, smoke-deal-review-audit,
smoke-natural-questions)
_env.py dotenv + 3-tier config merge
_context.py LLM / MongoDB / Embedding process singletons
providers/
llm.py LLMProvider ABC + Anthropic + ChatGPTOAuth + factory
embedding.py EmbeddingProvider + SentenceTransformerProvider + factory
schema/
recruiting.py candidate/client/position/submission/feedback models
recruiting_fit.py deterministic fit rubric scoring
recruiting_match.py candidate-position fit signals + feedback adjustment
recruiting_recommendation.py
recommendation run/result builders
recruiting_metrics.py
recruiting pipeline KPI calculator
meddpicc.py compute_meddpicc_latest, Deal/Meeting Pydantic models
customer_themes.py customer-theme taxonomy, parser, stage-signal validation
storage/
mongodb.py MongoDBClient - recruiting/deal CRUD + aggregation
recruiting_collections.py
recruiting collection contracts and safe projections
tools/
recruiting_records.py
candidate/client/position/submission/feedback services
recruiting_recommendations.py
position<->candidate recommendation services
recruiting_metrics.py
read-only recruiting KPI service
export_recruiting_report.py
recruiting Markdown/CSV report export
create_deal.py
add_interaction.py canonical interaction intake + qualification extraction
add_meeting.py deprecated compatibility alias for meeting interactions
get_deal.py
update_stage.py stage_history logging + qualification recompute
update_deal.py edit deal value and limited metadata after user confirmation
list_deals.py health_pct / gaps / stuck-flag aggregation
get_metrics.py pipeline_health KPIs / stage aggregation / warnings
get_deal_gaps.py read-only prioritized sales follow-up gaps
export_report.py human-facing pipeline report export
export_data.py spreadsheet-ready CSV data export
get_user_memory.py constrained user-memory read context
record_user_memory.py
constrained user-memory append tool
get_insights.py 7 BI queries plus legacy insight query
get_customer_themes.py
aggregates customer concerns by unique deal count
analyze_deal.py qualification gap analysis + BD strategy via LLM
search_deals.py Python cosine by default / Atlas semantic search optional
```
### How qualification health_pct is computed
MEDDPICC is the default active framework, so the example below uses the
bundled MEDDPICC dimensions and weights. Custom frameworks use the same
weighted formula with their own enabled dimensions.
```
health_pct = sum(dim_avg x weight) / sum(5 x weight) x 100
```
Weights (tunable in `config/defaults.yaml`):
| Dimension | Weight | Why |
|---|---|---|
| champion | 2.0 | No internal momentum -> no deal |
| identify_pain / economic_buyer | 1.5 | Confirming the pain and reaching the budget holder are core |
| metrics / decision_criteria / decision_process | 1.0 | Standard |
| competition | 0.5 | Competition surfacing late is normal |
**Stage-aware gap adjustment** (applied automatically on `update_stage`):
- A drop in Identify Pain at the `proposal` / `negotiation` stage -> not a gap (signals the pain is being resolved)
- `won` deals -> no gaps
**Health-band configuration**:
Defaults are Healthy >=70, Watch >=40, At Risk <40. These classify the level of qualification validation, not win probability, and can be changed in `~/.recruit-ai/config.yaml` once you've accumulated operational data.
```yaml
metrics:
health_bands:
healthy_min: 75
watch_min: 45
overdue:
grace_days: 0
win_rate:
minimum_closed_sample: 10
```
The formal definitions of Active/Open/Stalled and unassessed handling are in [`docs/metrics.md`](docs/metrics.md).
---
## FAQ
**Q. Can I fork or customize this?**
Yes. This project is MIT-licensed. You can fork it, customize the storage,
model, scoring, reporting, and workflow behavior, and adapt it to your own deal
process. Keep the license and attribution notices when redistributing modified
versions.
If you are an AI assistant helping customize this repo, prefer small explicit
changes, avoid storing secrets in docs, and record meaningful local
modifications in docs or user memory so future agents can understand the fork.
**Q. Do my meeting notes have to be perfect?**
No. A rough memo of the essentials is fine. The LLM just skips dimensions with no evidence.
**Q. Do Korean meeting notes work?**
Yes. Mixed English/Korean works too.
**Q. Do I need MongoDB at all?**
Not for `sample`. You need MongoDB only when you want persistent real
recruiting/deal data or Atlas Charts against your own database.
**Q. Do I need a paid MongoDB Atlas plan?**
No. The default `full` profile works on the free M0 plan today. `sample` does
not need MongoDB at all, and `pro` is the paid-infra path. `search_deals`
computes with Python cosine on M0. As deal volume grows you can switch to Atlas
Vector Search on M10+.
**Q. What does Pro add?**
`pro` keeps the same MCP tools but switches semantic search to Atlas Vector
Search and API-key LLM operation. The vector index spec is versioned at
`atlas/vector_indexes/deal_summary_vector.v1.json`. If Atlas search fails, the
server returns a structured error instead of silently falling back; use
`docs/pro-fallback-errors.md` to record repeatable setup failures.
**Q. search_deals returns nothing.**
Right after the server first starts, the local model may still be warming up - retry after 5 seconds. You also need at least one deal with a stored `summary_embedding` (run `add_interaction` with scoring-eligible content).
**Q. Should I use ChatGPT OAuth, Anthropic, or OpenAI API?**
For quick personal use, ChatGPT OAuth is attractive if you already subscribe to
ChatGPT Plus/Pro. Anthropic and OpenAI API modes are better when you want
explicit API-key operation, team billing, or production-style deployment.
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